neural coding & computation group
Principal Investigator  
Jonathan Pillow
Jonathan is an associate professor in the Princeton Neuroscience Institute (PNI) and Department of Psychology, with an affiliation to the Center for Statistics & Machine Learning. He received a Ph.D. in neural science from NYU (supervised by Eero Simoncelli), and was a postdoc at the Gatsby Computational Neuroscience Unit at UCL. Jonathan was an assistant professor at UT Austin (20092014) before moving to Princeton in Fall 2014.  
 
Postdocs  
Mikio Aio
Mikio has a Ph.D. in biomathematics from North Carolina State University, and joined the lab in February 2015. His current research focuses on scalable methods for receptive field estimation, dimensionality reduction methods for neural data, Bayesian optimization, Gaussian processes, and point process models for multivariate time series.  
Adam Charles
Adam has a Ph.D. in electrical and computer engineering at Georgia Tech, and joined the lab in June 2015. Adam's main research interests are in statistical signal processing, stochastic filtering, highdimensional probability and theoretical neuroscience. In particular, Adam is interested in how mathematical modeling can benefit both emerging imaging modalities across scientific disciplines as well as scientific theories on the behavior of biological neural systems.  
Ben Cowley
Ben has a PhD in machine learning from Carnegie Mellon University, and joined the lab in September 2018. Ben has a background in developing and applying dimensionality reduction techniques to neural data. Currently, he is focused on developing new adaptive stimulus selection algorithms to better train models that predict neural responses and behavior from stimulus features.  
Brian
DePasquale (joint with Carlos Brody) Brian completed a Ph.D. in Neurobiology and Behavior at Columbia University with Larry Abbott. His work focused on the dynamics of recurrent spiking and continuous variable neural networks and the development of methods for training these networks to perform tasks and to replicate experimental data. His current work focuses on latent variable models of behavior and neural activity during evidence accumulation. General areas of interests in theoretical neuroscience include the role of random and learned connections in neural circuit function and sources of variability in network dynamics and its relationship to behavior.  
Stephen Keeley
Stephen recently completed his Ph.D. at the Center for Neural Science at NYU under John Rinzel and Andre Fenton. His work involved using firing rate models to study competitive gamma oscillations in CA1 and the roles interneuron subtypes play in impacting gamma dynamics. Broadly, Stephen is interested in the dynamics of neuronal circuitry at different timescales, and how networkdictated dynamics and activitydependent circuit changes interact to shape learning, memory and behavior.  
 
Students  
Zoe
Ashwood Zoe is a second year Ph.D. student in the Computer Science department. Prior to coming to Princeton, Zoe obtained her undergraduate degree in math and physics at the University of St Andrews in Scotland, and worked for two years as a Research Fellow at Stanford. Zoe is interested in using Bayesian inference to find structure in neural spike train data and to improve experimental design.  
Farhan
Damani (joint with Ryan Adams) Farhan is a 2ndyear Ph.D. student in Computer Science with a B.S. degree from Johns Hopkins, where he worked with Alexis Battle on probabilistic models of genomic data. He is broadly interested in the development of statistical machine learning methods to better understand highdimensional neural spiking activity.  
Kevin Chen (joint with Andy Leifer) Kevin is a 2ndyear PhD student in PNI, with B.S. degree from National Taiwan University and M.S. research at Academia Sinica, where he studied predictive coding in the retina. After coming to Princeton, he was fascinated by neural dynamics and behavior in C. elegans. He is broadly interested in statistical models for animal behavior, biophysical models, and dynamics in neural networks.  
Mike Morais Mike is a 3rdyear PhD student in PNI, with B.S. degrees in Bioengineering and Mathematics from the University of Pittsburgh, where he studied attention and population coding in the primate visual system with Matt Smith. He also studied decision making in the mouse visual system at the RIKEN Brain Science Institute with Andrea Benucci. His research interests include Bayesian models of perception, dimension reduction for fMRI data, and associative learning models of psychological phenomena.  
Nicholas Roy Nick is a 4thyear Ph.D. student in PNI, with a B.S. in mathematics and computer science from Yale, where he studied population coding for working memory in the lab of XiaoJing Wang and under the supervision of John Murray. His research interests include adaptive optimal models of behavior and learning as well as functional connectivity estimation in C. elegans.  
Anqi Wu Anqi is a 4thyear Ph.D. student in PNI, with a B.S. in electrical engineering and an M.S. in computer science. She is currently working on statistical models of neural responses, highdimensional regression methods for fMRI decoding, and Bayesian optimization. Her research interests include latent variable models, active learning, and Bayesian methods in machine learning and statistics.  
David Zoltowski David is a 2ndyear Ph.D. student in PNI, with B.S. in electrical engineering from Michigan State University and an M.Phil. degree in Engineering from the University of Cambridge, where he worked with Máté Lengyel on perceptual decisionmaking. His research interests include statistical models of neural population and behavioral data and perceptual decisionmaking.  
 
 
Alumni  
